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unstructured/examples/sec-sentiment-analysis
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qued 007fc45739
chore: new black changes (#2473) 
Update `black` and apply changes to affected files. I separated this PR
so we can have a look at the changes and decide whether we want to:
1. Go forward with the new formatting
2. Change the black config to make the old formatting valid
3. Get rid of black entirely and just use `ruff`
4. Do something I haven't thought of
2024-01-30 17:12:35 +00:00
..
.gitignore
docs: example notebooks from community repo (#187)
2023-01-31 10:37:32 -05:00
fetch.py
chore: new black changes (#2473)
2024-01-30 17:12:35 +00:00
README.md
drop python3.8 (#2372)
2024-01-09 23:37:30 +00:00
requirements.in
build(deps): bump dependencies; resolve security issues in example dependencies (#300)
2023-02-27 12:45:28 -05:00
requirements.txt
build(deps): bump dependencies; resolve security issues in example dependencies (#300)
2023-02-27 12:45:28 -05:00
sec-sentiment-analysis.ipynb
docs: no more bricks (#1967)
2023-11-02 09:43:26 -05:00

README.md

SEC Sentiment Analysis Model

This directory contains an example of how to use the SEC API, the Unstructured SEC pipeline API, and several functions from the unstructured library to train a sentiment analysis model for the risk factors section of S-1 filings. To get started, use the following steps:

  • Ensure you have Python 3.10 or higher installed on your system
  • Create a new Python virtual environment
  • Run pip install -r requirements.txt to install the dependencies
  • Run PYTHONPATH=. jupyter notebook from this directory to launch the notebook

At this point, you'll be able to run the sentiment analysis example notebook.